regresjons

Regresjons is a term derived from the Norwegian word for regression and is commonly used to refer to statistical regression analysis, a set of techniques for modelling and analyzing the relationship between a dependent variable and one or more independent variables. The primary goal is to estimate the conditional expectation of the dependent variable given the independents, make predictions, or infer the strength and form of associations.

Common types of regression include linear regression, which models relationships with a straight line; logistic regression, used for binary outcomes; and more complex forms such as polynomial, ridge, lasso, and nonparametric regressions. Time-series regression and multilevel models extend the framework to temporal or hierarchical data structures.

Regression analysis relies on assumptions that vary by method but often include linearity, independence, homoscedasticity (constant variance), and correct model specification. Violations can lead to biased or inefficient estimates; common challenges include multicollinearity, heteroscedasticity, outliers, and overfitting.

Model evaluation uses measures such as R-squared, mean squared error, likelihood-based criteria, and cross-validation to assess fit and predictive performance. Interpretation emphasizes both statistical significance and practical effect sizes, with care to distinguish correlation from causation unless supported by experimental design or causal inference methods.

Historically, the idea of regression dates to 19th-century work on heredity and “regression to the mean”; modern regression developed through contributions by statisticians such as Francis Galton and Karl Pearson and remains foundational across sciences, economics, and machine learning.